Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide persona...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Neural computing & applications Ročník 36; číslo 11; s. 5695 - 5714
Hlavní autoři:	Shams, Mahmoud Y., Gamel, Samah A., Talaat, Fatma M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.04.2024 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Crop yield Data Mining and Knowledge Discovery Decision making Decision trees Explainable artificial intelligence Harnesses Image Processing and Computer Vision Machine learning Original Article Performance evaluation Probability and Statistics in Computer Science Recommender systems Agriculture Crop recommendation systems eXplainable artificial intelligence Decision support system Machine learning
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R 2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.
AbstractList	Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R 2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power. Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.
Author	Gamel, Samah A. Shams, Mahmoud Y. Talaat, Fatma M.
Author_xml	– sequence: 1 givenname: Mahmoud Y. surname: Shams fullname: Shams, Mahmoud Y. organization: Faculty of Artificial Intelligence, Kafrelsheikh University – sequence: 2 givenname: Samah A. surname: Gamel fullname: Gamel, Samah A. organization: Faculty of Engineering, Horus University – sequence: 3 givenname: Fatma M. orcidid: 0000-0001-6116-2191 surname: Talaat fullname: Talaat, Fatma M. email: fatma.nada@ai.kfs.edu.eg organization: Faculty of Artificial Intelligence, Kafrelsheikh University, Faculty of Computer Science and Engineering, New Mansoura University, Nile Higher Institute for Engineering and Technology
BookMark	eNp9kE1P3DAQhq2KSl2gf6AnSz0HxnY-nN4qxJeExIW7NXHGiyFxFtsR3X9fl62ExIHTHOZ95h09x-woLIEY-yHgTAB05wmgkaICqSroVS8q-YVtRK1UpaDRR2wDfV3Wba2-seOUngCgbnWzYa-X4RGD9WHLbVx2PJJd5pnCiNkvgad9yjQn_urzI6c_uwl9wGEijjF7563HifuQaZr8loKlXxx5yuu45wXGbfR2nfIaS2ok61M5Wc34XNpO2VeHU6Lv_-cJe7i6fLi4qe7ur28vft9VVrUqVxIcdaKz2umGHLRj3yGgAC3GGghRObJUy2GAoRudHtrRUt9Zi1o6RUqdsJ-Hs7u4vKyUsnla1hhKo5F902kQsq5LSh5SRUFKkZzZRT9j3BsB5p9fc_Bril_z5tfIAukPkPX5zVqO6KfPUXVAU-kJW4rvX31C_QVL0ZVa
CitedBy_id	crossref_primary_10_1002_cpe_70031 crossref_primary_10_1051_e3sconf_202562704003 crossref_primary_10_3390_plants14182841 crossref_primary_10_1016_j_atech_2025_101348 crossref_primary_10_1007_s00521_025_11038_3 crossref_primary_10_1016_j_atech_2025_101247 crossref_primary_10_1007_s44274_025_00327_2 crossref_primary_10_1088_2515_7620_adb9c0 crossref_primary_10_1109_ACCESS_2025_3571340 crossref_primary_10_1007_s41207_025_00876_1 crossref_primary_10_3390_jtaer20030194 crossref_primary_10_1016_j_jii_2025_100850 crossref_primary_10_1016_j_procs_2025_04_454 crossref_primary_10_3390_agronomy14102267 crossref_primary_10_3390_electronics13224362 crossref_primary_10_1007_s42979_025_04092_0 crossref_primary_10_1038_s41598_025_88676_z crossref_primary_10_1080_22797254_2024_2422330 crossref_primary_10_3390_s24196313 crossref_primary_10_1007_s00521_024_10208_z crossref_primary_10_1016_j_inffus_2024_102721 crossref_primary_10_1038_s41598_025_07003_8 crossref_primary_10_1007_s10068_025_01848_5 crossref_primary_10_3390_agriculture14081256 crossref_primary_10_1007_s00704_025_05505_z crossref_primary_10_1109_ACCESS_2025_3546681 crossref_primary_10_1007_s10489_025_06790_x crossref_primary_10_1016_j_atech_2025_101263 crossref_primary_10_3390_s25123583 crossref_primary_10_1007_s42452_024_06212_6 crossref_primary_10_1109_TCE_2025_3569736 crossref_primary_10_1007_s11760_025_04545_2 crossref_primary_10_1016_j_jjimei_2025_100328 crossref_primary_10_3390_math13172726 crossref_primary_10_1016_j_dajour_2025_100628 crossref_primary_10_3390_su17167382 crossref_primary_10_1038_s41598_025_14306_3 crossref_primary_10_3390_agriculture14060910 crossref_primary_10_54392_irjmt2525 crossref_primary_10_1109_ACCESS_2024_3451557 crossref_primary_10_1109_TAI_2024_3508654 crossref_primary_10_1007_s40745_024_00534_3
Cites_doi	10.3390/su15097114 10.3389/fnbot.2013.00021 10.21608/njccs.2022.280047 10.1016/j.rvsc.2010.08.006 10.1007/s00521-023-08619-5 10.1038/s41564-021-00880-5 10.32604/csse.2023.034324 10.1080/23302674.2023.2249808 10.1007/s11042-023-15803-1 10.1016/B978-0-12-822373-4.00018-5 10.1109/ICCUBEA.2018.8697349 10.1007/s12351-023-00752-2 10.3390/a16060308 10.1109/ICESC57686.2023.10192937 10.1088/1748-9326/aa518a 10.1016/j.asoc.2018.10.036 10.1109/RoboMech.2016.7813171 10.1177/0165551516677946 10.1016/j.eswa.2023.120135 10.1016/j.eja.2023.126949 10.1007/s12652-023-04659-w 10.1016/B978-0-12-816483-9.00001-3 10.1016/j.ins.2015.09.037 10.1214/aos/1013203451 10.3390/su15010757 10.1007/s11042-022-13000-0 10.1016/j.compag.2020.105709 10.3390/agronomy10071046 10.1007/s00521-023-08428-w 10.1073/pnas.0906865106 10.1145/3510858.3511382 10.1007/s11042-022-12223-5 10.1007/s11042-023-16737-4 10.1016/j.agrformet.2012.09.011 10.14569/IJACSA.2023.0140442 10.1109/ICSCDS56580.2023.10104734 10.1016/j.ecolind.2019.01.059 10.1007/s00500-022-07420-1 10.1609/aaai.v31i1.11172 10.1007/s00521-022-08007-59.FaivdullahL,AzaharF,HtikeZZ,Naing 10.1007/s00521-023-08678-8 10.1080/08839514.2021.1976091 10.1016/j.agsy.2020.103016 10.1007/s10878-023-01002-z 10.1007/978-3-031-22456-0_4 10.1115/1.4054800 10.1007/978-3-540-30549-1_43 10.1007/s00521-023-08372-9 10.1109/JSTARS.2020.3019046 10.32604/cmc.2022.026547 10.1016/j.aiia.2022.11.003 10.1007/s00521-023-08809-1 10.3389/fpls.2019.00621 10.32604/cmc.2023.039368 10.1088/1748-9326/aa7f33 10.1016/B978-0-444-52512-3.00005-X 10.12944/CARJ.11.1.12 10.3390/en14123396 10.1093/ajae/aas047 10.3390/bdcc7030144 10.21608/ejai.2023.205554.1008 10.1002/fld.5078 10.3390/bioengineering10010018
ContentType	Journal Article
Copyright	The Author(s) 2024 The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2024 – notice: The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	C6C AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI
DOI	10.1007/s00521-023-09391-2
DatabaseName	Springer Nature OA Free Journals CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition
DatabaseTitle	CrossRef Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	CrossRef Advanced Technologies & Aerospace Collection
Database_xml	– sequence: 1 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1433-3058
EndPage	5714
ExternalDocumentID	10_1007_s00521_023_09391_2
GrantInformation_xml	– fundername: Kafr El Shiekh University
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB DWQXO PKEHL PQEST PQQKQ PQUKI
ID	FETCH-LOGICAL-c363t-20fe717c8f85ef06d97a0a1081d40eaa3fece42bb0b7df8b6dce97cca82f3e33
IEDL.DBID	BENPR
ISICitedReferencesCount	43
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001140236500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0941-0643
IngestDate	Wed Nov 05 03:14:48 EST 2025 Tue Nov 18 22:38:18 EST 2025 Sat Nov 29 04:30:41 EST 2025 Fri Feb 21 02:39:49 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	Agriculture Crop recommendation systems eXplainable artificial intelligence Decision support system Machine learning
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c363t-20fe717c8f85ef06d97a0a1081d40eaa3fece42bb0b7df8b6dce97cca82f3e33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-6116-2191
OpenAccessLink	https://link.springer.com/10.1007/s00521-023-09391-2
PQID	2957801244
PQPubID	2043988
PageCount	20
ParticipantIDs	proquest_journals_2957801244 crossref_primary_10_1007_s00521_023_09391_2 crossref_citationtrail_10_1007_s00521_023_09391_2 springer_journals_10_1007_s00521_023_09391_2
PublicationCentury	2000
PublicationDate	20240400
PublicationDateYYYYMMDD	2024-04-01
PublicationDate_xml	– month: 4 year: 2024 text: 20240400
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	Neural computing & applications
PublicationTitleAbbrev	Neural Comput & Applic
PublicationYear	2024
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	RobertsMJBraunNOSinclairTRLobellDBSchlenkerWComparing and combining process-based crop models and statistical models with some implications for climate changeEnviron Res Lett20171292017ERL....12i5010R10.1088/1748-9326/aa7f33 FriedmanJHGreedy function approximation: a gradient boosting machineAnn Stat200129511891232187332810.1214/aos/1013203451 Alhussan AA, Talaat FM, El-kenawy ES, Abdelhamid AA, Ibrahim A, Khafaga DS, Alnaggar M (2023) Facial expression recognition model depending on optimized support vector machine. Comput Mater Continua. 76(1). Talaat FM (2022) Effective deep Q-networks (EDQN) strategy for resource allocation based on optimized reinforcement learning algorithm. Multimed Tools Appl 81(17). https://doi.org/10.1007/s11042-022-13000-0 FenzSNeubauerTHeurixJFriedelJKWohlmuthM-LAI- and data-driven pre-crop values and crop rotation matricesEur J Agron202315010.1016/j.eja.2023.126949 Doshi Z, Nadkarni S, Agrawal R, Shah N (2018) AgroConsultant: Intelligent crop recommendation system using machine learning algorithms. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Aug. 2018, pp. 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697349. ShamsMYElsheweyAMEl-kenawyE-SMIbrahimATalaatFMTarekZWater quality prediction using machine learning models based on grid search methodMultimed Tools Appl202310.1007/s11042-023-16737-436185324 Talaat Fatma M, Alshathri Samah, Nasr Aida A (2022) A new reliable system for managing virtualcloud network. Comput Mater Continua 73(3):5863–5885. https://doi.org/10.32604/cmc.2022.026547 GamelSAHassanEEl-RashidyNExploring the effects of pandemics on transportation through correlations and deep learning techniquesMultimed Tools Appl202310.1007/s11042-023-15803-13736273210244085 Pretorius A, Bierman S, Steel SJ (2016) A meta-analysis of research in random forests for classification. In: 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2016, pp. 1–6. Talaat FM (2023) Real-time facial emotion recognition system among children with autism based on deep learning and IoT. Neural Comput Appl 35(3), https://doi.org/10.1007/s00521-023-08372-9 You J, Li X, Low M, Lobell D, Ermon S (2017) Deep gaussian process for crop yield prediction based on remote sensing data. In: Thirty-First AAAI conference on artificial intelligence. AbbasFAfzaalHFarooqueAATangSCrop yield prediction through proximal sensing and machine learning algorithmsAgronomy2020107710.3390/agronomy10071046 Talaat FM, Gamel SA (2023) A2M-LEUK: attention-augmented algorithm for blood cancer detection in children, June 2023, Neural Comput Appl. https://doi.org/10.1007/s00521-023-08678-8 RosenzweigCThe agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studiesAgric For Meteorol20131701661822013AgFM..170..166R10.1016/j.agrformet.2012.09.011 XuSBayesian Naïve Bayes classifiers to text classificationJ Inf Sci2018441485910.1177/0165551516677946 TalaatFMCrop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changesNeural Comput Applic202310.1007/s00521-023-08619-5 El-RashidyNEbrahimNel GhamryATalaatFMUtilizing fog computing and explainable deep learning techniques for gestational diabetes predictionNeural Comput Applic202210.1007/s00521-022-08007-59.FaivdullahL,AzaharF,HtikeZZ,Naing ArifMSMukheimerAAsifDEnhancing the early detection of chronic kidney disease: a robust machine learning modelBig Data Cognit Comput202373310.3390/bdcc7030144 Elshewey A, Shams M, Tarek Z, Megahed M, El-kenawy E-S, El-dosuky M (2023) Weight prediction using the hybrid stacked-LSTM food selection model. CSSE, 46(1): 765–781, 2023, https://doi.org/10.32604/csse.2023.034324. RyoMExplainable artificial intelligence and interpretable machine learning for agricultural data analysisArtif Intell Agricult2022625726510.1016/j.aiia.2022.11.003 Natekin A, Knoll A (2022) Gradient boosting machines, a tutorial. Front Neurorobotics 7, 2013, Accessed: Sep. 27, 2022. Available: https://doi.org/10.3389/fnbot.2013.00021 SchlenkerWRobertsMJNonlinear temperature effects indicate severe damages to US crop yields under climate changeProc Natl Acad Sci20091063715594155982009PNAS..10615594S1:CAS:528:DC%2BD1MXhtFyjsLvP10.1073/pnas.0906865106197174322747166 NawazYArifMSAbodayehKA third-order two-stage numerical scheme for fractional stokes problems: a comparative computational studyJ Comput Nonlinear Dyn20221710.1115/1.4054800 JiangLWangSLiCZhangLStructure extended multinomial naive BayesInf Sci201632934635610.1016/j.ins.2015.09.037 de OliveiraACMariniNFariasDRClimate change: New breeding pressures and goalsEncyclopedia Agricult Food Syst2014201428429310.1016/B978-0-444-52512-3.00005-X TaleizadehAAAmjadianAHashemi-PetroodiSEMoonISupply chain coordination based on mean-variance risk optimisation: pricing, warranty, and full-refund decisionsInt J Syst Sci: Oper Logist2023101224980810.1080/23302674.2023.2249808 Mittal N, Bhanja A (2023) Implementation and identification of crop based on soil texture using AI. In: 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE. 1467–1471. XuXDesign of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province, ChinaEcol Ind201910194395310.1016/j.ecolind.2019.01.059 SiamAIGamelSATalaatFMAutomatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniquesNeural Comput Applic202310.1007/s00521-023-08428-w KhaterHAGamelSAEarly diagnosis of respiratory system diseases (RSD) using deep convolutional neural networksJ Ambient Intell Human Comput202314122731228310.1007/s12652-023-04659-w Sun J, Lai Z, Di L, Sun Z, Tao J, Shen Y (2020) Multilevel deep learning network for county-level corn yield estimation in the us corn belt. IEEE J Selected Top Appl Earth Obs Shams MY, Tarek Z, Elshewey AM, Hany M, Darwish A, Hassanien AE (2023) A machine learning-based model for predicting temperature under the effects of climate change. In: The Power of Data: Driving Climate Change with Data Science and Artificial Intelligence Innovations, A. E. Hassanien and A. Darwish, Eds., in Studies in Big Data. Cham: Springer Nature Switzerland, 2023: 61–81. https://doi.org/10.1007/978-3-031-22456-0_4. RaoHFeature selection based on artificial bee colony and gradient boosting decision treeAppl Soft Comput20197463464210.1016/j.asoc.2018.10.036 Van KlompenburgTKassahunACatalCCrop yield prediction using machine learning: A systematic literature reviewComput Electron Agric202017710.1016/j.compag.2020.105709 Talaat FM (2022) Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system. Multimed Tools Appl PaudelDMachine learning for large-scale crop yield forecastingAgric Syst202118710.1016/j.agsy.2020.103016 TaleizadehAAVarziAMAmjadianANoori-daryanMKonstantarasIHow cash-back strategy affect sale rate under refund and customers’ creditOper Res Int J20232311910.1007/s12351-023-00752-2 Freund Y, Mason L (1999) The alternating decision tree learning algorithm. In: Icml, 1999, 99, pp. 124–133. TarekZSoil erosion status prediction using a novel random forest model optimized by random search methodSustainability2023159910.3390/su15097114 Khaki S, Wang L (2022) Crop Yield Prediction Using Deep Neural Networks. Front Plant Sci 10, 2019, Accessed: Sep. 27, 2022. Available: https://doi.org/10.3389/fpls.2019.00621 https://www.kaggle.com/datasets/ananysharma/crop-yield. Ke G, et al. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: Advances in Neural Information Processing Systems, 2017, 30. Accessed: Sep. 27, 2022. Available: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html Sun C, Li X, Guo R (2021) Research on electrical fire risk assessment technology of cultural building based on random forest algorithm. In: 2021 International Conference on Aviation Safety and Information Technology, New York, NY, USA, Dec. 2021, pp. 769–773. https://doi.org/10.1145/3510858.3511382. TalaatFMZainEldinHAn improved fire detection approach based on YOLO-v8 for smart citiesNeural Comput Applic202310.1007/s00521-023-08809-1 Alnaggar M, Handosa M, Medhat T, Z Rashad M (2023) Thyroid Disease multi-class classification based on optimized gradient boosting model. Egypt J Artif Intell. 2(1):1–4. El-RashidyNElSayedNEEl-GhamryATalaatFMPrediction of gestational diabetes based on explainable deep learning and fog computingSoft Comput20222621114351145010.1007/s00500-022-07420-1 HanaaSFatmaBTDetection and classification using deep learning and sine-cosine fitnessgrey wolf optimizationBioengineering20221011810.3390/bioengineering10010018 BaliNSinglaADeep learning based wheat crop yield prediction model in punjab region of north indiaAppl Artif Intell202135151304132810.1080/08839514.2021.1976091 Feng J, Yu Y, Zhou Z-H (2018) Multi-Layered Gradient Boosting Decision Trees. In: Advances in Neural Information Processing Systems, 2018, 31. Accessed: Sep. 27, 2022. Available: https://proceedings.neurips.cc/paper/2018/hash/39027dfad5138c9ca0c474d71db915c3-Abstract.html GeenenPLvan der GaagLCLoeffenWLAElbersARWConstructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine feverRes Vet Sci201191164701:STN:280:DC%2BC3MnkvFajug%3D%3D10.1016/j.rvsc.2010.08.00620817220 Hassan E, El-Rashidy N, Talaat FM (2022) Review: Mask R-CNN Models. May 2022, https://doi.org/10.21608/njccs.2022.280047. RobertsMJSchlenkerWEyerJAgronomic weather measures in econometric models of crop yield with implications for climate changeAm J Agr Econ201395223624310.1093/ajae/aas047 Williams TO, et al. (2015) Climate smart agriculture in the African context. Unlocking Africa’s Agricultural Potentials for Transformation to Scale , FAO and UNEP , Abdou Diouf International Conference, Dakar, Senegal, D Asif (9391_CR20) 2023; 16 9391_CR61 9391_CR65 9391_CR63 9391_CR62 F Abbas (9391_CR33) 2020; 10 9391_CR69 9391_CR24 AA Taleizadeh (9391_CR54) 2023; 23 MJ Roberts (9391_CR14) 2017; 12 9391_CR17 9391_CR16 AC de Oliveira (9391_CR25) 2014; 2014 M Ryo (9391_CR50) 2022; 6 C Rosenzweig (9391_CR10) 2013; 170 S Xu (9391_CR44) 2018; 44 A Gharaei (9391_CR53) 2023; 45 9391_CR71 9391_CR70 9391_CR32 9391_CR30 MS Arif (9391_CR19) 2023; 7 9391_CR36 9391_CR28 9391_CR27 T Van Klompenburg (9391_CR4) 2020; 177 9391_CR26 H Rao (9391_CR38) 2019; 74 Y Nawaz (9391_CR22) 2022; 17 S Hanaa (9391_CR60) 2022; 10 AI Siam (9391_CR64) 2023 9391_CR1 9391_CR7 SA Gamel (9391_CR66) 2023 9391_CR5 N El-Rashidy (9391_CR58) 2022; 26 9391_CR9 DB Lobell (9391_CR12) 2017; 12 L Jiang (9391_CR46) 2016; 329 X Xu (9391_CR2) 2019; 101 9391_CR42 9391_CR41 MY Shams (9391_CR49) 2023 9391_CR40 9391_CR47 S Fenz (9391_CR18) 2023; 150 JH Friedman (9391_CR35) 2001; 29 9391_CR45 9391_CR39 9391_CR37 FM Talaat (9391_CR34) 2023 W Schlenker (9391_CR13) 2009; 106 PL Geenen (9391_CR43) 2011; 91 Y Nawaz (9391_CR23) 2022; 94 FM Talaat (9391_CR67) 2023 D Paudel (9391_CR29) 2021; 187 M Alnaggar (9391_CR68) 2023; 1 9391_CR51 9391_CR57 N Bali (9391_CR3) 2021; 35 Y Nawaz (9391_CR21) 2021; 14 9391_CR56 9391_CR55 HS Yoon (9391_CR31) 2021; 6 AM Elshewey (9391_CR8) 2023; 15 N El-Rashidy (9391_CR59) 2022 MJ Roberts (9391_CR15) 2013; 95 9391_CR48 HA Khater (9391_CR11) 2023; 14 AA Taleizadeh (9391_CR52) 2023; 10 Z Tarek (9391_CR6) 2023; 15
References_xml	– reference: Talaat FM (2023) Real-time facial emotion recognition system among children with autism based on deep learning and IoT. Neural Comput Appl 35(3), https://doi.org/10.1007/s00521-023-08372-9 – reference: Freund Y, Mason L (1999) The alternating decision tree learning algorithm. In: Icml, 1999, 99, pp. 124–133. – reference: RyoMExplainable artificial intelligence and interpretable machine learning for agricultural data analysisArtif Intell Agricult2022625726510.1016/j.aiia.2022.11.003 – reference: AbbasFAfzaalHFarooqueAATangSCrop yield prediction through proximal sensing and machine learning algorithmsAgronomy2020107710.3390/agronomy10071046 – reference: SiamAIGamelSATalaatFMAutomatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniquesNeural Comput Applic202310.1007/s00521-023-08428-w – reference: You J, Li X, Low M, Lobell D, Ermon S (2017) Deep gaussian process for crop yield prediction based on remote sensing data. In: Thirty-First AAAI conference on artificial intelligence. – reference: Van KlompenburgTKassahunACatalCCrop yield prediction using machine learning: A systematic literature reviewComput Electron Agric202017710.1016/j.compag.2020.105709 – reference: NawazYArifMSShatanawiWNazeerAAn explicit fourth-order compact numerical scheme for heat transfer of boundary layer flowEnergies20211412121:CAS:528:DC%2BB3MXhsl2hs7bP10.3390/en14123396 – reference: ShamsMYElsheweyAMEl-kenawyE-SMIbrahimATalaatFMTarekZWater quality prediction using machine learning models based on grid search methodMultimed Tools Appl202310.1007/s11042-023-16737-436185324 – reference: Sun J, Lai Z, Di L, Sun Z, Tao J, Shen Y (2020) Multilevel deep learning network for county-level corn yield estimation in the us corn belt. IEEE J Selected Top Appl Earth Obs – reference: Ke G, et al. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: Advances in Neural Information Processing Systems, 2017, 30. Accessed: Sep. 27, 2022. Available: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html – reference: NawazYArifMSAbodayehKAn explicit-implicit numerical scheme for time fractional boundary layer flowsInt J Numer Meth Fluids2022947920940444813210.1002/fld.5078 – reference: JiangLWangSLiCZhangLStructure extended multinomial naive BayesInf Sci201632934635610.1016/j.ins.2015.09.037 – reference: GamelSAHassanEEl-RashidyNExploring the effects of pandemics on transportation through correlations and deep learning techniquesMultimed Tools Appl202310.1007/s11042-023-15803-13736273210244085 – reference: ElsheweyAMA novel WD-SARIMAX model for temperature forecasting using daily Delhi climate datasetSustainability2023151110.3390/su15010757 – reference: RobertsMJBraunNOSinclairTRLobellDBSchlenkerWComparing and combining process-based crop models and statistical models with some implications for climate changeEnviron Res Lett20171292017ERL....12i5010R10.1088/1748-9326/aa7f33 – reference: Kibriya AM, Frank E, Pfahringer B, Holmes G (2005) Multinomial naive bayes for text categorization revisited. In: AI 2004: Advances in Artificial Intelligence, Berlin, Heidelberg, 2005, pp. 488–499. https://doi.org/10.1007/978-3-540-30549-1_43. – reference: TarekZSoil erosion status prediction using a novel random forest model optimized by random search methodSustainability2023159910.3390/su15097114 – reference: Talaat FM (2022) Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system. Multimed Tools Appl – reference: ArifMSMukheimerAAsifDEnhancing the early detection of chronic kidney disease: a robust machine learning modelBig Data Cognit Comput202373310.3390/bdcc7030144 – reference: Talaat Fatma M, Alshathri Samah, Nasr Aida A (2022) A new reliable system for managing virtualcloud network. Comput Mater Continua 73(3):5863–5885. https://doi.org/10.32604/cmc.2022.026547 – reference: KhaterHAGamelSAEarly diagnosis of respiratory system diseases (RSD) using deep convolutional neural networksJ Ambient Intell Human Comput202314122731228310.1007/s12652-023-04659-w – reference: LobellDBAssengSComparing estimates of climate change impacts from process-based and statistical crop modelsEnviron Res Lett20171212017ERL....12a5001L10.1088/1748-9326/aa518a – reference: RobertsMJSchlenkerWEyerJAgronomic weather measures in econometric models of crop yield with implications for climate changeAm J Agr Econ201395223624310.1093/ajae/aas047 – reference: BaliNSinglaADeep learning based wheat crop yield prediction model in punjab region of north indiaAppl Artif Intell202135151304132810.1080/08839514.2021.1976091 – reference: PaudelDMachine learning for large-scale crop yield forecastingAgric Syst202118710.1016/j.agsy.2020.103016 – reference: TaleizadehAAAmjadianAHashemi-PetroodiSEMoonISupply chain coordination based on mean-variance risk optimisation: pricing, warranty, and full-refund decisionsInt J Syst Sci: Oper Logist2023101224980810.1080/23302674.2023.2249808 – reference: Porter JR, Xie L, Challinor AJ, Cochrane K, Howden SM, Iqbal MM, Lobell DB, Travasso MI (2014) Food security and food production systems. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1(1): 485–533 (2014). – reference: HanaaSFatmaBTDetection and classification using deep learning and sine-cosine fitnessgrey wolf optimizationBioengineering20221011810.3390/bioengineering10010018 – reference: TalaatFMCrop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changesNeural Comput Applic202310.1007/s00521-023-08619-5 – reference: Doshi Z, Nadkarni S, Agrawal R, Shah N (2018) AgroConsultant: Intelligent crop recommendation system using machine learning algorithms. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Aug. 2018, pp. 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697349. – reference: TalaatFMZainEldinHAn improved fire detection approach based on YOLO-v8 for smart citiesNeural Comput Applic202310.1007/s00521-023-08809-1 – reference: Williams TO, et al. (2015) Climate smart agriculture in the African context. Unlocking Africa’s Agricultural Potentials for Transformation to Scale , FAO and UNEP , Abdou Diouf International Conference, Dakar, Senegal, pp. 1–26, 2015. – reference: AlnaggarMSiamAIHandosaMMedhatTRashadMZVideo-based real-time monitoring for heart rate and respiration rateExpert Syst Appl2023122510.1016/j.eswa.2023.120135 – reference: XuXDesign of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province, ChinaEcol Ind201910194395310.1016/j.ecolind.2019.01.059 – reference: AsifDBibiMArifMSMukheimerAEnhancing heart disease prediction through ensemble learning techniques with hyperparameter optimizationAlgorithms2023166610.3390/a16060308 – reference: GharaeiAAmjadianAShavandiAAmjadianAAn augmented Lagrangian approach with general constraints to solve nonlinear models of the large-scale reliable inventory systemsJ Comb Optim202345278456507410.1007/s10878-023-01002-z – reference: McEldowney JF (2021) Climate change and the law. In: the impacts of climate change, Elsevier. 503–519. – reference: TaleizadehAAVarziAMAmjadianANoori-daryanMKonstantarasIHow cash-back strategy affect sale rate under refund and customers’ creditOper Res Int J20232311910.1007/s12351-023-00752-2 – reference: de OliveiraACMariniNFariasDRClimate change: New breeding pressures and goalsEncyclopedia Agricult Food Syst2014201428429310.1016/B978-0-444-52512-3.00005-X – reference: FenzSNeubauerTHeurixJFriedelJKWohlmuthM-LAI- and data-driven pre-crop values and crop rotation matricesEur J Agron202315010.1016/j.eja.2023.126949 – reference: Shams MY, Tarek Z, Elshewey AM, Hany M, Darwish A, Hassanien AE (2023) A machine learning-based model for predicting temperature under the effects of climate change. In: The Power of Data: Driving Climate Change with Data Science and Artificial Intelligence Innovations, A. E. Hassanien and A. Darwish, Eds., in Studies in Big Data. Cham: Springer Nature Switzerland, 2023: 61–81. https://doi.org/10.1007/978-3-031-22456-0_4. – reference: El-RashidyNElSayedNEEl-GhamryATalaatFMPrediction of gestational diabetes based on explainable deep learning and fog computingSoft Comput20222621114351145010.1007/s00500-022-07420-1 – reference: Pretorius A, Bierman S, Steel SJ (2016) A meta-analysis of research in random forests for classification. In: 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2016, pp. 1–6. – reference: Mittal N, Bhanja A (2023) Implementation and identification of crop based on soil texture using AI. In: 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE. 1467–1471. – reference: https://www.kaggle.com/datasets/ananysharma/crop-yield. – reference: Alnaggar M, Handosa M, Medhat T, Z Rashad M (2023) Thyroid Disease multi-class classification based on optimized gradient boosting model. Egypt J Artif Intell. 2(1):1–4. – reference: Elshewey A, Shams M, Tarek Z, Megahed M, El-kenawy E-S, El-dosuky M (2023) Weight prediction using the hybrid stacked-LSTM food selection model. CSSE, 46(1): 765–781, 2023, https://doi.org/10.32604/csse.2023.034324. – reference: GeenenPLvan der GaagLCLoeffenWLAElbersARWConstructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine feverRes Vet Sci201191164701:STN:280:DC%2BC3MnkvFajug%3D%3D10.1016/j.rvsc.2010.08.00620817220 – reference: Sun C, Li X, Guo R (2021) Research on electrical fire risk assessment technology of cultural building based on random forest algorithm. In: 2021 International Conference on Aviation Safety and Information Technology, New York, NY, USA, Dec. 2021, pp. 769–773. https://doi.org/10.1145/3510858.3511382. – reference: Talaat FM (2022) Effective deep Q-networks (EDQN) strategy for resource allocation based on optimized reinforcement learning algorithm. Multimed Tools Appl 81(17). https://doi.org/10.1007/s11042-022-13000-0 – reference: SchlenkerWRobertsMJNonlinear temperature effects indicate severe damages to US crop yields under climate changeProc Natl Acad Sci20091063715594155982009PNAS..10615594S1:CAS:528:DC%2BD1MXhtFyjsLvP10.1073/pnas.0906865106197174322747166 – reference: RaoHFeature selection based on artificial bee colony and gradient boosting decision treeAppl Soft Comput20197463464210.1016/j.asoc.2018.10.036 – reference: Alhussan AA, Talaat FM, El-kenawy ES, Abdelhamid AA, Ibrahim A, Khafaga DS, Alnaggar M (2023) Facial expression recognition model depending on optimized support vector machine. Comput Mater Continua. 76(1). – reference: Alnaggar M, Handosa M, Medhat T, Rashad MZ (2023) An IoT-based framework for detecting heart conditions using machine learning. Int J Adv Comput Sci Appl. 14(4). – reference: Reddy PS, Amarnath B, Sankari M (2023) Study on machine learning and back propagation for crop recommendation system. In: 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), IEEE. 1533–1537. – reference: Natekin A, Knoll A (2022) Gradient boosting machines, a tutorial. Front Neurorobotics 7, 2013, Accessed: Sep. 27, 2022. Available: https://doi.org/10.3389/fnbot.2013.00021 – reference: Alpaydin E (2020) Introduction to machine learning. MIT press. – reference: FriedmanJHGreedy function approximation: a gradient boosting machineAnn Stat200129511891232187332810.1214/aos/1013203451 – reference: Feng J, Yu Y, Zhou Z-H (2018) Multi-Layered Gradient Boosting Decision Trees. In: Advances in Neural Information Processing Systems, 2018, 31. Accessed: Sep. 27, 2022. Available: https://proceedings.neurips.cc/paper/2018/hash/39027dfad5138c9ca0c474d71db915c3-Abstract.html – reference: Patel K, Patel HB (2023) Multi-criteria agriculture recommendation system using machine learning for crop and fertilizesrs prediction. Curr Agricult Res J 11(1), 2023. – reference: NawazYArifMSAbodayehKA third-order two-stage numerical scheme for fractional stokes problems: a comparative computational studyJ Comput Nonlinear Dyn20221710.1115/1.4054800 – reference: XuSBayesian Naïve Bayes classifiers to text classificationJ Inf Sci2018441485910.1177/0165551516677946 – reference: Khaki S, Wang L (2022) Crop Yield Prediction Using Deep Neural Networks. Front Plant Sci 10, 2019, Accessed: Sep. 27, 2022. Available: https://doi.org/10.3389/fpls.2019.00621 – reference: Hassan E, El-Rashidy N, Talaat FM (2022) Review: Mask R-CNN Models. May 2022, https://doi.org/10.21608/njccs.2022.280047. – reference: RosenzweigCThe agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studiesAgric For Meteorol20131701661822013AgFM..170..166R10.1016/j.agrformet.2012.09.011 – reference: El-RashidyNEbrahimNel GhamryATalaatFMUtilizing fog computing and explainable deep learning techniques for gestational diabetes predictionNeural Comput Applic202210.1007/s00521-022-08007-59.FaivdullahL,AzaharF,HtikeZZ,Naing – reference: YoonHSAkkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in miceNat Microbiol20216551:CAS:528:DC%2BB3MXotVamsLc%3D10.1038/s41564-021-00880-5 – reference: Talaat FM, Gamel SA (2023) A2M-LEUK: attention-augmented algorithm for blood cancer detection in children, June 2023, Neural Comput Appl. https://doi.org/10.1007/s00521-023-08678-8 – reference: Bhadouria R, et al. (2019) Agriculture in the era of climate change: Consequences and effects. In Climate Change and Agricultural Ecosystems, Elsevier, 1–23. – reference: Talaat FM (2023) Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes, April 2023, Neural Comput Appl 35(2), https://doi.org/10.1007/s00521-023-08619-5 – volume: 15 start-page: 9 issue: 9 year: 2023 ident: 9391_CR6 publication-title: Sustainability doi: 10.3390/su15097114 – ident: 9391_CR36 doi: 10.3389/fnbot.2013.00021 – ident: 9391_CR63 doi: 10.21608/njccs.2022.280047 – volume: 91 start-page: 64 issue: 1 year: 2011 ident: 9391_CR43 publication-title: Res Vet Sci doi: 10.1016/j.rvsc.2010.08.006 – year: 2023 ident: 9391_CR34 publication-title: Neural Comput Applic doi: 10.1007/s00521-023-08619-5 – volume: 6 start-page: 5 issue: 5 year: 2021 ident: 9391_CR31 publication-title: Nat Microbiol doi: 10.1038/s41564-021-00880-5 – ident: 9391_CR48 doi: 10.32604/csse.2023.034324 – volume: 10 start-page: 2249808 issue: 1 year: 2023 ident: 9391_CR52 publication-title: Int J Syst Sci: Oper Logist doi: 10.1080/23302674.2023.2249808 – ident: 9391_CR62 doi: 10.1007/s00521-023-08619-5 – year: 2023 ident: 9391_CR66 publication-title: Multimed Tools Appl doi: 10.1007/s11042-023-15803-1 – ident: 9391_CR24 doi: 10.1016/B978-0-12-822373-4.00018-5 – ident: 9391_CR39 – ident: 9391_CR51 doi: 10.1109/ICCUBEA.2018.8697349 – volume: 23 start-page: 19 issue: 1 year: 2023 ident: 9391_CR54 publication-title: Oper Res Int J doi: 10.1007/s12351-023-00752-2 – volume: 16 start-page: 6 issue: 6 year: 2023 ident: 9391_CR20 publication-title: Algorithms doi: 10.3390/a16060308 – ident: 9391_CR17 doi: 10.1109/ICESC57686.2023.10192937 – volume: 12 issue: 1 year: 2017 ident: 9391_CR12 publication-title: Environ Res Lett doi: 10.1088/1748-9326/aa518a – ident: 9391_CR40 – volume: 74 start-page: 634 year: 2019 ident: 9391_CR38 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2018.10.036 – ident: 9391_CR41 doi: 10.1109/RoboMech.2016.7813171 – volume: 44 start-page: 48 issue: 1 year: 2018 ident: 9391_CR44 publication-title: J Inf Sci doi: 10.1177/0165551516677946 – volume: 1 issue: 225 year: 2023 ident: 9391_CR68 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.120135 – volume: 150 year: 2023 ident: 9391_CR18 publication-title: Eur J Agron doi: 10.1016/j.eja.2023.126949 – volume: 14 start-page: 12273 year: 2023 ident: 9391_CR11 publication-title: J Ambient Intell Human Comput doi: 10.1007/s12652-023-04659-w – ident: 9391_CR1 doi: 10.1016/B978-0-12-816483-9.00001-3 – volume: 329 start-page: 346 year: 2016 ident: 9391_CR46 publication-title: Inf Sci doi: 10.1016/j.ins.2015.09.037 – volume: 29 start-page: 1189 issue: 5 year: 2001 ident: 9391_CR35 publication-title: Ann Stat doi: 10.1214/aos/1013203451 – volume: 15 start-page: 1 issue: 1 year: 2023 ident: 9391_CR8 publication-title: Sustainability doi: 10.3390/su15010757 – ident: 9391_CR55 doi: 10.1007/s11042-022-13000-0 – ident: 9391_CR26 – ident: 9391_CR47 – volume: 177 year: 2020 ident: 9391_CR4 publication-title: Comput Electron Agric doi: 10.1016/j.compag.2020.105709 – volume: 10 start-page: 7 issue: 7 year: 2020 ident: 9391_CR33 publication-title: Agronomy doi: 10.3390/agronomy10071046 – year: 2023 ident: 9391_CR64 publication-title: Neural Comput Applic doi: 10.1007/s00521-023-08428-w – volume: 106 start-page: 15594 issue: 37 year: 2009 ident: 9391_CR13 publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.0906865106 – ident: 9391_CR42 doi: 10.1145/3510858.3511382 – ident: 9391_CR56 doi: 10.1007/s11042-022-12223-5 – year: 2023 ident: 9391_CR49 publication-title: Multimed Tools Appl doi: 10.1007/s11042-023-16737-4 – volume: 170 start-page: 166 year: 2013 ident: 9391_CR10 publication-title: Agric For Meteorol doi: 10.1016/j.agrformet.2012.09.011 – ident: 9391_CR70 doi: 10.14569/IJACSA.2023.0140442 – ident: 9391_CR37 – ident: 9391_CR27 doi: 10.1109/ICSCDS56580.2023.10104734 – volume: 101 start-page: 943 year: 2019 ident: 9391_CR2 publication-title: Ecol Ind doi: 10.1016/j.ecolind.2019.01.059 – volume: 26 start-page: 11435 issue: 21 year: 2022 ident: 9391_CR58 publication-title: Soft Comput doi: 10.1007/s00500-022-07420-1 – ident: 9391_CR28 doi: 10.1609/aaai.v31i1.11172 – year: 2022 ident: 9391_CR59 publication-title: Neural Comput Applic doi: 10.1007/s00521-022-08007-59.FaivdullahL,AzaharF,HtikeZZ,Naing – ident: 9391_CR65 doi: 10.1007/s00521-023-08678-8 – volume: 35 start-page: 1304 issue: 15 year: 2021 ident: 9391_CR3 publication-title: Appl Artif Intell doi: 10.1080/08839514.2021.1976091 – volume: 187 year: 2021 ident: 9391_CR29 publication-title: Agric Syst doi: 10.1016/j.agsy.2020.103016 – volume: 45 start-page: 78 issue: 2 year: 2023 ident: 9391_CR53 publication-title: J Comb Optim doi: 10.1007/s10878-023-01002-z – ident: 9391_CR7 doi: 10.1007/978-3-031-22456-0_4 – volume: 17 year: 2022 ident: 9391_CR22 publication-title: J Comput Nonlinear Dyn doi: 10.1115/1.4054800 – ident: 9391_CR45 doi: 10.1007/978-3-540-30549-1_43 – ident: 9391_CR61 doi: 10.1007/s00521-023-08372-9 – ident: 9391_CR30 doi: 10.1109/JSTARS.2020.3019046 – ident: 9391_CR57 doi: 10.32604/cmc.2022.026547 – volume: 6 start-page: 257 year: 2022 ident: 9391_CR50 publication-title: Artif Intell Agricult doi: 10.1016/j.aiia.2022.11.003 – year: 2023 ident: 9391_CR67 publication-title: Neural Comput Applic doi: 10.1007/s00521-023-08809-1 – ident: 9391_CR9 – ident: 9391_CR32 doi: 10.3389/fpls.2019.00621 – ident: 9391_CR71 doi: 10.32604/cmc.2023.039368 – volume: 12 issue: 9 year: 2017 ident: 9391_CR14 publication-title: Environ Res Lett doi: 10.1088/1748-9326/aa7f33 – volume: 2014 start-page: 284 year: 2014 ident: 9391_CR25 publication-title: Encyclopedia Agricult Food Syst doi: 10.1016/B978-0-444-52512-3.00005-X – ident: 9391_CR16 doi: 10.12944/CARJ.11.1.12 – volume: 14 start-page: 12 issue: 12 year: 2021 ident: 9391_CR21 publication-title: Energies doi: 10.3390/en14123396 – volume: 95 start-page: 236 issue: 2 year: 2013 ident: 9391_CR15 publication-title: Am J Agr Econ doi: 10.1093/ajae/aas047 – volume: 7 start-page: 3 issue: 3 year: 2023 ident: 9391_CR19 publication-title: Big Data Cognit Comput doi: 10.3390/bdcc7030144 – ident: 9391_CR5 – ident: 9391_CR69 doi: 10.21608/ejai.2023.205554.1008 – volume: 94 start-page: 920 issue: 7 year: 2022 ident: 9391_CR23 publication-title: Int J Numer Meth Fluids doi: 10.1002/fld.5078 – volume: 10 start-page: 18 issue: 1 year: 2022 ident: 9391_CR60 publication-title: Bioengineering doi: 10.3390/bioengineering10010018
SSID	ssj0004685
Score	2.5932436
Snippet	Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	5695
SubjectTerms	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Crop yield Data Mining and Knowledge Discovery Decision making Decision trees Explainable artificial intelligence Harnesses Image Processing and Computer Vision Machine learning Original Article Performance evaluation Probability and Statistics in Computer Science Recommender systems
SummonAdditionalLinks	– databaseName: SpringerLink Contemporary dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3LS8MwHA4yPXhxPnE6JQdvGmibrk28iWx4kCE6ZLeS5xxsdazz8eebpGk3RQU9N4-S_J5Jft8HwBkX1PohjkQaaGQ8tEaESoYi45t5wggTyoG43qb9PhkO6Z0vCiuq1-7VlaSz1HWxmz3BNKlvhJHJwmmIjOFdN9MQq473D48r1ZCOiNPkLfZNT4x9qcz3Y3x2R8sY88u1qPM2veb__nMbbPnoEl6V4rAD1lS-C5oVcwP0irwH3rr5kwXayEfQUnhBmxZPp8oTLMES3rmA9pAWqvfZxJdYQStnJeQEHK9geV5CBh1OLTSd2Whe43lA6Sl80NSxXu2DQa87uL5BnoIBCZzghdEhrUzCJ4gmHaWDRNKUBSw0cYSMA8UY1kqoOOI84KnUhCdSKJoaqSCRxgrjA9DIn3N1CGCHRYm1L4FkYawIJ6xDUiow4cwEjVK3QFhtRCY8PLllyZhkNbCyW9jMLGzmFjaLWuC87jMrwTl-bd2u9jfzilpkETUmK7BBTgtcVPu5_PzzaEd_a34MNiMTDpVvftqgsZi_qBOwIV4X42J-6gT4A3rw7Ks priority: 102 providerName: Springer Nature
Title	Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making
URI	https://link.springer.com/article/10.1007/s00521-023-09391-2 https://www.proquest.com/docview/2957801244
Volume	36
WOSCitedRecordID	wos001140236500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1433-3058 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1433-3058 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV27TsMwFL2CwsDCG1EoyAMbWKR2mjgsCFARA6qqghBiifwEJBpKWx6fj-04LSDBwpIhiaNI59r3Yd9zAPaEzJwfElimkcHWQxvMMsUxsb5ZJJxxqT2J62Xa6bDb26wbCm6jcKyyWhP9Qq2epauRH5LM2lbkvNHx4AU71Si3uxokNGZhzjGVxTWYO213ur0vnZFelNPmMO58T0xD24xvnnMVUXuXUGyz-qyJyXfXNI03f2yRes9zvvTff16GxRBzopPSSFZgRhersFTpOaAwvdfgvV08OPqN4h45YS_kkuV-XwfZJVSSPo-QK90i_TF4Co1XyFlfSUSBHr8wfB4hjjx7LbKD-f1wwvKBVBD2wX2vhbUO1-ft67MLHIQZsKQJHduZZbRNAyUzrKVNlKgs5RFv2uhCxZHmnBotdUyEiESqDBOJkjpLra0wYqimdANqxXOhNwG1OEncqhMp3ow1E4y3WJpJygS3oaQydWhWkOQykJY77YynfEK37GHMLYy5hzEnddifjBmUlB1_vt2osMvD9B3lU-DqcFChP338-9e2_v7aNiwQGxSVJ38aUBsPX_UOzMu38eNouBuMdxdmu607e-1d3XwC_sj6MA
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LTtwwFL1CUKndQOlDnUKLF-2qtZpxMoldqaqqFgRiOmIxC9SN5SdFYsIwM-XxUfxjrx1nBpDKjkW3SWwpybkPX_ueA_BOGxHikKamyjzFCO0pF1ZRhrFZl4or4yKJa78aDPjhoThYguu2FyYcq2x9YnTU9tSEGvknJhBbWYhGX8dnNKhGhd3VVkKjgcW-u7rAJdv0y94P_L_vGdvZHn7fpUlVgJq8zGcIC-9wDWO45z3ns9KKSmWqi6HRFplTKvfOuIJpnenKeq5La5yo8EU587kL9U_0-CtFgWslNJ-D3q8bbZhRARQXTOEwUZGnHp3YqRfKr3iV5TQTuehSdjsOLpLbO_uxMcztrP1nH-gprKZ8mnxrDGAdllz9DNZarQqSXNdzuNiufwdqkfqIBNEyEgoBo5FLklKkIbSeklCWJu5yfJKaykiwrIZkgxzfYC_9TBSJzLwEB6ujyZzBhNgkWkRHUefrBQwf4u1fwnJ9WrtXQHqKlcGjZlZ1C8c1Vz1eCZNzrTBNtr4D3RYB0iRC9qALciLnVNIRNRJRIyNqJOvAh_mYcUNHcu_Tmy1UZHJNU7nASQc-tmBb3P73bK_vn20LHu8Of_Zlf2-wvwFPGCZ_zQmnTVieTf64N_DInM-Op5O30WoIyAcG4V_3m1bB
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA4yRXxxXnE6NQ--aVjXdG3qm-iG4hgDh-yt5DoHWx1bvfx8kzTdpqggPjcJ5ZyTc0lyvg-AM8ZjE4cY4pGnkI7QCpFYUOTr2MxCSiiXFsS1HXU6pN-Pu0td_Pa1e3Elmfc0GJSmNKtNhKrNG9_MaaYug32MdEUe15F2wquBIQ0y9frD41JnpCXl1DWMed8TYNc28_0an0PTIt_8ckVqI0-r_P9_3gKbLuuEV7mZbIMVme6AcsHoAN0G3wVvzfTJAHCkA2iovaApl8dj6YiXYA77PIPm8BbK98nItV5BY385FAUcLmF8XkIKLX4t1JPpYDrH-YDCUfugsWXD2gO9VrN3fYscNQPiOMSZ3ltK6kKQE0UaUnmhiCPqUa2Augg8SSlWksvAZ8xjkVCEhYLLONLWQnyFJcb7oJQ-p_IAwAb1Q-N3PEHrgSSM0AaJYo4JozqZFKoC6oVSEu5gyw17xiiZAy5bwSZasIkVbOJXwPl8ziQH7fh1dLXQdeI28CzxY-3KPJP8VMBFodvF559XO_zb8FOw3r1pJe27zv0R2PB1xpQ_C6qCUjZ9kcdgjb9mw9n0xNr1B1l3-HM
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Enhancing+crop+recommendation+systems+with+explainable+artificial+intelligence%3A+a+study+on+agricultural+decision-making&rft.jtitle=Neural+computing+%26+applications&rft.au=Shams%2C+Mahmoud+Y&rft.au=Gamel%2C+Samah+A&rft.au=Talaat%2C+Fatma+M&rft.date=2024-04-01&rft.pub=Springer+Nature+B.V&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=36&rft.issue=11&rft.spage=5695&rft.epage=5714&rft_id=info:doi/10.1007%2Fs00521-023-09391-2
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon